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Micro-expressions are brief and subtle facial expressions that go on and offthe face in a fraction of a second. This kind of facial expressions usuallyoccurs in high stake situations and is considered to reflect a human's realintent. There has been some interest in micro-expression analysis, however, agreat majority of the methods are based on classically established computervision methods such as local binary patterns, histogram of gradients andoptical flow. A novel methodology for micro-expression recognition using theRiesz pyramid, a multi-scale steerable Hilbert transform is presented. In fact,an image sequence is transformed with this tool, then the image phasevariations are extracted and filtered as proxies for motion. Furthermore, thedominant orientation constancy from the Riesz transform is exploited to averagethe micro-expression sequence into an image pair. Based on that, the MeanOriented Riesz Feature description is introduced. Finally the performance ofour methods are tested in two spontaneous micro-expressions databases andcompared to state-of-the-art methods.

Mean Oriented Riesz Features for Micro Expression Classification